System and method for un-biasing user personalizations and recommendations

ABSTRACT

Disclosed are systems and methods for an electronic framework that enables un-biasing of personalizations for users. The disclosed framework provides controls that can enable users to selectively escape from previously conceived notions of a user&#39;s preferred tastes and/or interests. Upon a user requesting content, the disclosed framework can analyze the type of request as well as the modeled behavior and preferences of the user, and automatically un-bias or depersonalize content for the user, thereby availing the user to a broader range of content from a larger pool of content then previously made available to the user.

BACKGROUND

Content providers, services, applications, websites, platforms and otherforms of network resources that users access to consume and/or interactwith content typically provide such visiting users with personalizedcontent.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, and advantages of the disclosure will be apparent from thefollowing description of embodiments as illustrated in the accompanyingdrawings, in which reference characters refer to the same partsthroughout the various views. The drawings are not necessarily to scale,emphasis instead being placed upon illustrating principles of thedisclosure:

FIG. 1 is a block diagram of an example network architecture accordingto some embodiments of the present disclosure:

FIG. 2 is a block diagram illustrating components of an exemplary systemaccording to some embodiments of the present disclosure;

FIG. 3 illustrates an exemplary data flow according to some embodimentsof the present disclosure:

FIG. 4 illustrates a non-limiting example of a user interface (UI)according to some embodiments of the present disclosure;

FIG. 5 illustrates a non-limiting example of a UI according to someembodiments of the present disclosure;

FIG. 6 illustrates an exemplary data flow according to some embodimentsof the present disclosure;

FIG. 7 illustrates an exemplary data flow according to some embodimentsof the present disclosure;

FIG. 8 illustrates an exemplary data flow according to some embodimentsof the present disclosure:

FIG. 9 illustrates an exemplary data flow according to some embodimentsof the present disclosure;

FIG. 10 illustrates an exemplary data flow according to some embodimentsof the present disclosure;

FIG. 11 illustrates an exemplary data flow according to some embodimentsof the present disclosure;

FIG. 12 illustrates an exemplary data flow according to some embodimentsof the present disclosure;

FIG. 13 illustrates a non-limiting example of a UI according to someembodiments of the present disclosure; and

FIG. 14 is a block diagram illustrating a computing device showing anexample of a client or server device used in various embodiments of thepresent disclosure.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The disclosed systems and methods provide a framework thatdepersonalizes content for users. That is, currently almost all of thecontent that is searchable and/or provided to requesting users ispersonalized. The personalization can be based on, but is not limitedto, the user's derived or expressed interests, demographic information,biographic information, online and/or real-world behaviors, and thelike. Personalization of content for a user spans, for example, movies,shows, news, books, music, web sites, retail products, sports, and thelike.

Over time, systems learn a lot about a user's tastes and may end upproviding the user only with things that are similar to what the systemhas understood as the user being interested in (e.g., what the user iswatching, consuming and/or purchasing). This can lead to a userconsistently being presented with only a small fraction of the overallcontent that is actually available for consumption. Moreover, this canresult in the user being “pigeon-holed” in a cycle of the systemproviding the same content to the user without an opportunity for theuser to expand beyond his/her perceived niche.

The disclosed systems and methods provide a framework that providesusers with an ability to customize their own personalizations. That is,the framework enables users to control personalization of the contentthey are interacting with in order to avoid biasing of the content thatis being provided and/or made available to the user. Rather than simplyturning off personalization, the disclosed framework can analyze thetype of request as well as the modeled behavior and preferences of auser, and un-bias or break (or free) the user from the preconceivednotions of what the user is expected to be interested in by providingthe user with a broader range of content from a larger pool of contentthen previously made available to the user.

FIG. 1 is a block diagram of an example network architecture accordingto some embodiments of the present disclosure. In the illustratedembodiment. UE 102 accesses a data network 108 via an access network 104and a core network 106. In the illustrated embodiment, UE 102 comprisesany computing device capable of communicating with the access network104. As examples, UE 102 may include mobile phones, tablets, laptops,sensors, Internet of Things (IoT) devices, autonomous machines, UAVs,wired devices, wireless handsets, and any other devices equipped with acellular or wireless or wired transceiver. One non-limiting example of aUE is provided in FIG. 14 .

In the illustrated embodiment of FIG. 1 , the access network 104comprises a network allowing network communication with UE 102. Ingeneral, the access network 104 includes at least one base station thatis communicatively coupled to the core network 106 and coupled to zeroor more UEs 102.

In some embodiments, the access network 104 comprises a cellular accessnetwork, for example, a fifth-generation (5G) network or afourth-generation (4G) network. In one embodiment, the access network104 can comprise a NextGen Radio Access Network (NG-RAN), which can becommunicatively coupled to UE 102. In an embodiment, the access network104 may include a plurality of base stations (e.g., eNodeB (eNB), gNodeB(gNB)) communicatively connected to UE 102 via an air interface. In oneembodiment, the air interface comprises a New Radio (NR) air interface.For example, in a 5G network, UE 102 can be communicatively coupled toeach other via an X2 interface.

In the illustrated embodiment, the access network 104 provides access toa core network 106 to the UE 102. In the illustrated embodiment, thecore network may be owned and/or operated by a network operator (NO) andprovides wireless connectivity to UE 102 via access network 104. In theillustrated embodiment, this connectivity may comprise voice and dataservices.

At a high-level, the core network 106 may include a user plane and acontrol plane. In one embodiment, the control plane comprises networkelements and communications interfaces to allow for the management ofuser connections and sessions. By contrast, the user plane may comprisenetwork elements and communications interfaces to transmit user datafrom UE 102 to elements of the core network 106 and to externalnetwork-attached elements in a data network 108 such as, but not limitedto, the Internet, a local area network (LAN), a wireless LAN, a widearea network (WAN), a mobile edge computing (MEC) network, a privatenetwork, a cellular network, and the like.

In the illustrated embodiment, the access network 104 and the corenetwork 106 may be operated by a NO. However, in some embodiments, thenetworks (104, 106) may be operated by a private entity, differentenmities, and the like, and may be closed to public traffic. In theseembodiments, the operator of the device can simulate a cellular network,and UE 102 can connect to this network similar to connecting to anational or regional network.

FIG. 1 further includes recommendation engine 200 which can beconfigured for enabling and/or performing anti-biasing of userpersonalizations, as discussed below in relation to FIGS. 3-13 . In someembodiments, recommendation engine 200 can be a special purpose machineor processor, and can be hosted by or integrated into functionalityassociated with access network 104, core network 106 and/or data network108, or some combination thereof.

In some embodiments, recommendation engine 200 can be hosted by any typeof network server, such as, but not limited to, an edge node or server,application server, content server, web server, and the like, or anycombination thereof.

In some embodiments, recommendation engine 200 can be embodied as astand-alone application that executes on a networking server. In someembodiments, recommendation engine 200 can function as an applicationinstalled on a user's device, and in some embodiments, such applicationcan be a web-based application accessed by the user device over anetwork. In some embodiments, recommendation engine 200 can beconfigured and/or installed as an augmenting script, program orapplication (e.g., a plug-in or extension) to another application orportal data structure.

As illustrated in FIG. 2 , recommendation engine 200 can include, but isnot limited to, request module 202, explore module 204, determinationmodule 206 and output module 208. It should be understood that theengine(s) and modules discussed herein are non-exhaustive, as additionalor fewer engines and/or modules (or sub-modules) may be applicable tothe embodiments of the systems and methods discussed.

According to some embodiments, recommendation engine 200 enables analternative set and/or a broader set of content to be recommended and/orprovided to a user in response to an indication from the user thatrelates to a request and/or desire to interact with or receive content.More detail of the operations, configurations and functionalities ofengine 200 and each of its modules, and their role within embodiments ofthe present disclosure will be discussed below in relation to FIGS. 3-13.

FIG. 3 provides Process 300 which details non-limiting exampleembodiments of engine 200's implementation for recommending un-biasedcontent to a user. As discussed herein, Process 300 of FIG. 3 provides ageneral overview of engine 200's operation, and FIGS. 600-1200 in FIGS.6-12 , respectively, provide non-limiting examples of particularun-biasing embodiments.

According to some embodiments, Steps 302-304 of Process 300 can beperformed by request module 202 of recommendation engine 200; Steps306-308 can be performed by explore module 204, Step 310 can beperformed by determination module 206, and Steps 312-316 can beperformed by output module 208.

Process 300 begins with Step 302 where a request from a user for contentis received. In some embodiments, this request can be any type ofrequest, input or detected criteria that causes content to be discoveredand provided to a user. For example, the request can include, but is notlimited to, a search query, detection of the user being at a particularlocation, a time criteria being satisfied, conditional notifications,opening an application that provides content, visiting a web page,logging into a portal, and/or any other type of request that causespersonalized content to be provided to a user.

In Step 304, a set of personalized content is provided to the user.According to some embodiments, Step 304 can include, but is not limitedto, engine 200 receiving the request, and providing a recommended set ofcontent items to a user by using a defined recommendation model, whichcan involve utilizing any type of known or to be known intelligent,artificial intelligence (AI) or machine learning (ML) contentpersonalization model, such as, but not limited to, neural networks,computer vision, vector analysis, and the like. The recommendation modelcan comprise learned and/or trained personalization (or taste orinterest) vectors that indicate the user's tastes, interests, behaviorsand/or activities (e.g., real-world and digital actions), and the like.

According to some embodiments, Step 304 involves executing therecommendation model which operates by analyzing of the request from theuser, which can include analysis of the data in the request, as well asthe data and/or metadata about the user, and formulating a query tosearch for requested content. The search can involve parsing a storedcollection of content, and matching, at least to a similarity thresholdlevel, content data and/or metadata to the user and/or the user'srequest. As a result, the personalized set of content can be provided tothe user.

By way of a non-limiting example, as depicted in the user interface (UI)400 of FIG. 4 , items 402-410 represent a set of 5 content items thatcan be the personalized set of content from Step 304. For example, items402-410 can be, but are not limited to, image thumbnails that representmovies on a streaming platform that are being recommended to the user.According to some embodiments, UI 400 further provides an un-biasing (orbreak or escape) option 412. In some embodiments, option 412 can be abutton, icon or any other type of selectable or interactive interfaceobject that can be displayed, embedded, or otherwise depicted within UI400. Thus, UI 400 can be modified to additionally display option 412when providing recommended content.

In some embodiments, option 412 can enable other forms of input to bereceived and/or accepted by UI 400. For example, in some embodiments,option 412 may not be displayed on UI 400, but can representfunctionality where if a user provides a voice input request to beun-biased or have provided content be depersonalized, then thecapabilities of options 412 are triggered, as discussed below.

It should be understood that the arrangement and orientation of items402-412 on UI 400 are not intended to be limiting, as content 402-410and option 412 (e g., icon or button) can be provided/displayedaccording to any type of known or to be known graphical user interface(GUI) functionality available on web pages, interfaces, applicationsand/or devices upon which user's view and/or consume content.

Turning back to Process 300 of FIG. 3 , in Step 306, engine 200 receivesa request to have provided content be un-biased or depersonalized. Asdiscussed above, this can correspond a user requesting a broader and/oralternative selection of content to be recommended or made available tothem. As discussed above in relation to FIG. 4 , this can involveproviding an un-biasing input, which for example, can involveinteracting with option 412 (e.g., button) displayed on UI 400. In someembodiments, this can also involve selecting which recommended items theuser desires to “escape” from (e g., all or a portion of items 402410),as discussed below.

According to some embodiments, upon receiving the request in Step 306,engine can provide a set of options that enables a particular type ofun-biasing (or depersonalization). For example, as illustrated in UI 500depicted in FIG. 5 , items 502-518 (which can be buttons, icons orinterface objects) can be displayed on UI 500. In some embodiments, UI500 can be a modified version of UI 400 such that items 502-518 can beadded to UI 400 to realize UI 500. Thus, interaction with option 412 onUI 400 can result in UI 500. In some embodiments, UI 500 can be aseparately displayed window or interface. In some embodiments, items402-410 may not be displayed within UI 500.

According to some embodiments, items 502-518 provide capabilities forexecuting specific un-biasing methods. For example, interaction with (orselection of) item 502 provides capabilities for un-biasing the resultsfrom Step 304 (and items 402-410) based on “examples”, as discussed inmore detail below in relation to FIG. 6 . Similarly, item 504 enablesun-biasing based on “attributes”, as discussed below in relation to FIG.7 . Item 506 enables un-biasing based on “user clusters”, as discussedbelow in relation to FIG. 8 . Item 508 enables un-biasing based on“sentiment”, as discussed below in relation to FIG. 9 . Item 510 enables“random” un-biasing, as discussed below in relation to FIG. 10 . Items512 and 514 enable un-biasing based on “popularity” and/or “trend”,respectively, as discussed below in relation to FIG. 11 (and referred toas “exposure”). Item 516 enables un-biasing based on a “timeline”, asdiscussed below in relation to FIG. 12 .

In some embodiments, item 518 enables complete un-biasing of any type ofpersonalization. According to some embodiments, this can involvere-performing the search for content for a user as if this is the firsttime the user is performing a search and engine 200 does not have anydata or metadata about the user (e.g., a new user which does not haveany stored information within accessible databases, for example).

By way of a non-limiting example, upon a user being provided arecommended set of movies (items 402-410), the user interacts withoption 412 thereby requesting to be un-biased or depersonalized fromthese recommendations. UI 500 is provided to the user, which enables theuser to specifically select items they desire to break or escape from(e.g., items 404, 408 and 410, for example, as illustrated in FIG. 5 ).UI 500 further provides the user with selection options via items502-518 for performing specific types of un-biasing, as discussed below.

Thus, according to some embodiments, the request in Step 306 includesinformation that indicates a desire for a user to escape from a set ofpreviously provided recommendations (and/or criteria that governs how arecommendation model recommends content to the user), and furtheridentifies a type of escape being requested. In some embodiments, therequest in Step 306 can identify specific items that can form a basis ofthe un-biasing request, as discussed below in more detail.

In Step 308, engine 200 analyzes the set of personalized content (fromStep 304) based on the information associated with the un-biasingrequest (from Step 306), and based on this analysis, in Step 310, a newset of content is searched for and identified, which in Step 312 ispresented to the user. In some embodiments, at least a portion of thenew set of content items identified in Step 310 comprisescharacteristics that are different from characteristics from the contentidentified and provided in Step 304, as discussed below. Non-limitingexample embodiments of the specific operations of Steps 308-312 arediscussed in detail below in relation to Processes 600-1200 of FIGS.6-12 , respectively.

According to some embodiments, in Step 314, the information related tothe new set of content and/or the information provided in the un-biasingrequest (from Step 306) can be stored in association with the user. InStep 316, the recommendation model can be further trained (orre-trained, used interchangeably) based on this information so as toprovide automatic un-biasing for the user for future searches, asdiscussed below. In some embodiments, as discussed below, a new modelcan be defined for use on future recommendation searches for the user.

Turning to FIG. 6 . Process 600 provides a non-limiting exampleembodiment of performing an un-biasing request that corresponds to an“example” (item 502 of FIG. 5 , as discussed above). According to someembodiments, Steps 602-612 of Process 600 can be performed by exploremodule 204 of recommendation engine 200 (and provide non-limitingexample embodiments of Steps 306-312 of Process 300); and Steps 614-616can be performed by output module 208.

Process 600 begins with Step 602 where an input can be provided by theuser that corresponds to an “example” type of depersonalization. In someembodiments, this involves determining similarities and differencesbetween examples of items for purposes of expanding types of contentbeing provided to a user, as discussed herein. For example, Step 602 caninvolve a user selecting item 502 from UI 500, as discussed above.

In Step 604, an example item(s) (or one or more example items) can beidentified. In some embodiments, this can involve the identification ofitems from a previously provided set of recommendations—for example, auser can select at least one item from items 402-410, as discussedabove. For purposes of this discussion, only a single example item willbe discussed as being identified in Step 604; however, it should not beconstrued as limiting any as any number of items (or a whole set ofitems from a set of previously recommended content items) can beidentified without departing from the scope of the instant disclosure.

In Step 606, based on the identified example item, a similarity groupand an opposite group of items can be presented to the user. That is, aset of items that are within a threshold satisfying similarity value tothe example item are identified (e.g., similarity group), and a set ofitems that are within a threshold satisfying opposite value to theexample item are identified (e.g., opposite group, which includescontent items that are opposite of the example). In some embodiments,the “threshold satisfying opposite value” corresponds to the similarityvalue that the set of items do not satisfy.

For example, the example item identified in Step 604 is a televisionbroadcast of a NFL® game. The similarity group can include similar typesof broadcast sports, such as, but not limited to, games from the NBA®,NHL®, NASCAR® and other action-based sports or activities. The oppositegroup, for example, can include other sports that would be deemed theopposite of the high-action and physicality of football, such as, forexample, PGA® golf broadcasts, MLB® games, track and field, and thelike. In another example, opposite items to an NFL® game can also betelevision shows that are not sports related (e.g., sitcoms, talk shows,and the like).

According to some embodiments, Step 606 can involve engine 200 utilizinga vector engine that translates the example item (from Step 604) into an-dimensional feature vector. Engine 200 can then compute similaritiesand opposites of at least a portion of the n-dimensions of the exampleitem's feature vector to identify a similarity grouping and an oppositegrouping. For example, while NASCAR® races may not have all similarattributes to a football game, it may satisfy particular attributes thatconstitute a type of viewing experience (e g., fast paced, high action,drama, and the like). Thus, when comparing the vectors between the NFL®game to the NASCAR® race, the race can be deemed to fall within asimilarity group because at least a threshold satisfying number ofdimensions along the race's vector match the football game's vector. Ina similar manner, opposite content items can be discovered based on athreshold satisfying number of dimensions are found to be not matching,or matching below a similarity threshold, as discussed above.

In Step 608, upon presentation of the similarity and opposite groupingsof content items, the user can have the ability to select (e.g., like)or remove (or unselect or dislike) a set (e.g., one or more) of itemsfrom each group. For example, the user can identify a content item fromthe similarity grouping they are not interested in, which could causethat item to be removed from the similarity group. In a similar manner,the user can identify a content item from the opposite group that theywould like to view (despite them being the opposite of what wasrecommended).

According to some embodiments, Step 608 can be optional, whereby Process600 can proceed from Step 606 to Step 610.

In Step 610, each item (remaining, if Step 608 is enabled) in thesimilarity and opposite groupings are analyzed. According to someembodiments, the analysis performed in Step 610 can be performed byengine 200 executing a vector analysis model that analyzes the items ineach grouping (e.g., feature vectors of each item are analyzed) anddetermines their corresponding attributes. In some embodiments, theanalysis can also be performed by any other known or to be knowncomputational analysis technique and/or AI/ML classifier, algorithm,mechanism or technology, including, but not limited to, computer vision,neural networks, cluster analysis, data mining, Bayesian networkanalysis, Hidden Markov models, logical models and/or tree analysis, andthe like.

In Step 612, based on the analysis from Step 610, a determination ofsimilarities and differences between the items in each group isperformed. In some embodiments, Step 610 can involve determiningsimilarities and differences between, but not limited to, items thatwere selected and/or removed in the similarity group to the items thatwere selected and/or removed in the opposite group (as discussed abovein Step 608).

In Step 614, a new content recommendation can be identified and providedto the user. The content items included in the recommendation can beidentified and provided based on the determinations from Step 612, andin a similar manner as discussed above. According to some embodiments,the determined similarities and differences between the items in eachgroup, as determined in Step 612, can be utilized as training data totrain a new recommendation model, or re-train the existingrecommendation model (used in Step 304 for the initial recommendation).Thus, the new model or re-trained model can be used to identify andpresent the content to the user in Step 614.

In Step 616, the training data and/or information related to thenew/re-trained model can be stored in an associated database in asimilar manner as discussed above in relation to Step 314.

In FIG. 7 , Process 700 is disclosed which provides a non-limitingexample embodiment for performing an un-biasing request that correspondsto an “attribute” (item 504 of FIG. 5 , as discussed above). Accordingto some embodiments, Steps 702-716 of Process 700 can be performed byexplore module 204 of recommendation engine 200 (and providenon-limiting example embodiments of Steps 306-312 of Process 300); andSteps 718-720 can be performed by output module 208.

Process 700 begins with Step 702 where an input can be provided by theuser that corresponds to an “attribute” (or attributes) type ofdepersonalization. In some embodiments, this involves identifying anattribute(s) (e.g., a characteristic or feature) of a content item(s)that can be excluded or “escaped” from during content selection, asdiscussed herein. For example, Step 702 can involve a user selectingitem 504 from UI 500, as discussed above.

In Step 704, a set of attributes are determined, derived or otherwiseidentified. In some embodiments, the attributes that can be derived canbe based on at least one of the items in the recommended content set(from Step 304) and/or the user's current taste or interest (orpersonalization) information (which can be formulated as a tastevector(s) (or interest or personalization vector(s), usedinterchangeably)).

In Step 706, the determined attribute set can be presented to the user,whereby, in Step 708, a selection of at least one attribute is received.For purposes of this discussion, a single attribute will be discussed asbeing selected; however, it should not be construed as limiting as aplurality of attributes can be selected which would not depart from thedisclosed scope of the instant disclosure.

In Step 710, engine 200 analyzes the attribute, and determines the typeof attribute that was selected in Step 708. In some embodiments, thetype of attribute can be, but is not limited to, a boolean attribute (eg., true or false—whether the element exists or is requested, or whetherit does not exist or is not requested), a categorical attribute, and anumerical attribute (e.g., values for each type of content that describethe content).

When it is determined that the attribute is a boolean attribute, Process700 proceeds from Step 710 to Step 712 where engine 200 automaticallycomputes an escape in a boolean manner. For example, if the attributecorresponds to a “sports” game (e.g., “true” boolean value), thenescaping that attribute in a boolean manner provides content or anattribute(s) that is not that attribute (e.g., “false” boolean value)for example, any type of content that is not “sports” related content,such as, for example, a soap-opera television show.

In some embodiment, the determined information from Step 712 can then beused to identify and present new content to the user, as in Step 718.Moreover, in a similar manner as discussed above, the informationrelated to the new content recommendation and the boolean attributeinformation from Step 712 can be stored and used train a new modeland/or re-train the recommendation model, as in Step 720.

When it is determined that the attribute is categorical, Process 700proceeds from Step 710 to Step 714 where engine 200 automaticallydetermines and presents categories that can be related to the attributeso that the user can select the categories to escape from. For example,if the attribute corresponds a NFL® football game, then the determinedcategories can be “sports” and “football”.

In some embodiment, the determined information from Step 714 can then beused to identify and present new content to the user, as in Step 718.Moreover, in a similar manner as discussed above, the informationrelated to the new content recommendation and the categorical attributeinformation from Step 714 can be stored and used to train a new modeland/or re-train the recommendation model, as in Step 720.

When it is determined that the attribute is numerical, Process 700proceeds from Step 710 to Step 716 where engine 200 automaticallydetermines attributes or content with attributes that have values thatare at least a threshold distance to the selected attribute's value(from Step 708). In some embodiments, the distance calculations can bebased on vector analysis computations executed by a vector analysis MLmodel. This can enable the identification of content that is minimallyopposite the content the user is attempting to escape from. For example,if an attribute corresponds to “sports”, then “comedy” may be anattribute that is a distance far enough from “sports” to providealternative or broader content options.

In some embodiment, the determined information from Step 716 can then beused to identify and present new content to the user, as in Step 718.Moreover, in a similar manner as discussed above, the informationrelated to the new content recommendation and the numerical attributeinformation from Step 716 can be stored and used train a new modeland/or re-train the recommendation model, as in Step 720.

In FIG. 8 , Process 800 is disclosed which provides a non-limitingexample embodiment for performing an un-biasing request that correspondsto “user clusters” (item 506 of FIG. 5 , as discussed above). Accordingto some embodiments, Steps 802-810 of Process 800 can be performed byexplore module 204 of recommendation engine 200 (and providenon-limiting example embodiments of Steps 306-312 of Process 300); andSteps 812-814 can be performed by output module 208.

Process 800 begins with Step 802 where an input can be provided by theuser that corresponds to a “user clusters” type of depersonalization.Thus, for example, Step 802 can involve a user selecting item 506 fromUI 500, as discussed above. In some embodiments, this can involveidentifying user clusters, or cluster of users and/or tastes/intereststhat the user is currently affiliated with.

By way of a non-limiting example, a user cluster can be associated witha group of users that are interested in the same type of content and/orare from the same geographic region or demographic grouping, or othertype of data that can be used to group similar users and/or similaractivities of users. In some embodiments, such cluster can berepresented by n-dimensional vectors that represent values of interestand/or affiliation of the user to each topic or context that eachcluster represents.

In Step 804, a set of other user clusters are identified and presentedto the user. According to some embodiments, the other clusters can beclusters that are similar and/or opposite to the user cluster (of Step802), which can be determined in a similar manner as discussed above (atleast in relation to Process 600, above). For example, clusters that areassociated with different contexts than the user cluster that isassociated with the user, as discussed above in relation to Step 802.

According to some embodiments, Step 804 can involve a user cluster (fromStep 802), which as discussed above, can be a vector, can be representedas V_(i)=(s1, s2, s3, . . . , sm), where “s” represents data objects ofusers falling within cluster vector V. In some embodiments, to identifythe other clusters, centroids of the cluster data can be determinedwhich can be used to identify which other clusters (and/or the itemstherein) correspond to the centroids at least within (for similarity) orat least beyond (for opposites) a threshold distance. For example, anidentification of centroids for a cluster can then be performed. Forexample, a centroid “u” for s1 of Vi can be: (u1_(s1), u1_(s2), u1_(s3),. . . , u1_(sm)). For s2, s3, . . . , and sm, the same form of centroidcan be identified: u2, u3, . . . , and um, respectively. Engine 200 canthen compute which content items, and/or other user clusters are similaror opposite to the computed centroids. In some embodiments, this can beperformed by engine 200 performing vector analysis techniques todetermine which vector nodes have similar or different dimensionalvalues in view of a similarity threshold.

Thus, in Step 804, a set of items can be presented to the user, whichcan correspond to at least one of similarly related user clusters oropposite user clusters.

In Step 806, a selection is received of an item from at least one othercluster. In some embodiments, the selection can indicate selection orremoval of an item from a user cluster, in a similar manner as discussedabove. In some embodiments, the selection can be associated with anentire and/or portion of a presented cluster. While the discussionherein focuses on selection of a single item, it should be understoodthat multiple items and multiple clusters can be presented and selectedwithout departing from the scope of the instant disclosure.

In Step 808, a vector associated with the selected item is identified;and in Step 810, taste vectors for the user are adjusted. In someembodiments, the adjustment of the user's vectors can be based on theircurrent vector, the vector of the selected item, and/or centroids of thevectors (e.g., centroid of the vector the user selected, for example).

For example, the user's current taste vector is Vi, as discussed above.The vector for the selected item is Vk. Therefore, the new vectorV_(new) can be realized by: (Vi+Vk)/2.

In some embodiments, if multiple clusters “V1, V2, . . . Vr” areselected (from Step 806), then V_(new) can equal: (Vi+(V1+V2+ . . .Vr)/r)/2.

In Step 812, the adjusted vectors are then used to present new contentrecommendations to the user. Moreover, in a similar manner as discussedabove, the information related to the new content recommendation and thevector adjustment from Step 810 can be stored and used to train a newmodel and/or re-train the recommendation model, as in Step 814.

Thus, Process 800 enables users to escape from recommendations that aretied to vector data associated with clustered data related to the user'sactivities and tastes.

In FIG. 9 , Process 900 is disclosed which provides a non-limitingexample embodiment for performing an un-biasing request that correspondsto a “sentiment” (or mood) (item 508 of FIG. 5 , as discussed above).According to some embodiments, Steps 902-912 of Process 900 can beperformed by explore module 204 of recommendation engine 200 (andprovide non-limiting example embodiments of Steps 306-312 of Process300), and Steps 914-916 can be performed by output module 208.

Process 900 begins with Step 902 where an input can be provided by theuser that corresponds to a “sentiments” type of depersonalization. Forexample, Step 902 can involve a user selecting item 508 from UI 500, asdiscussed above. In some embodiments, Step 902 (and item 508) can enablea user to input a sentiment to escape from, as discussed below.

In some embodiments, “sentiments” (or “moods”) can correspond to, butare not limited to, expressions of user preferences, emotions, feelings,tastes, and the like. For example, a sentiment can be a form of data,metadata (e.g., a tag) that indicates a feeling towards an item. In someembodiments, the sentiment can correspond to comments about items,feedback, rendering history, and the like, or some combination thereof.In some embodiments, sentiment data can be derived, extracted,identified or determined from data and/or metadata related to a contentitem (e g., data associated with a content item, or data identifiablefrom pages where the content item is hosted, for example).

In Step 904, engine 200 can analyze the selected content item from items402-410 (from Step 306 from which the request 902 is based), anddetermine sentiment data related to the selected content item. While thediscussion herein is focused on a single content item, it should not beconstrued as limiting, as embodiments exist without departing from thescope of the instant disclosure for performing Process 900 based onmultiple selected content items.

In some embodiments, Step 904 can then involve identifying a set of userclusters that correspond to the determined sentiment. This can be basedon a similarity analysis of the sentiment of the selected contentitem(s) and the data associated with the user clusters.

In some embodiments, Step 904 can involve engine 200 utilizing naturallanguage processing (NLP) models to extract sentiment information fromthe data and/or metadata of the selected content item. In someembodiments, sentiment analysis models, such as, but not limited to,Naïve Bayes, Logistic Regression and Support Vector Machines (SVM), forexample, can be applied to the NLP processed information to determine asentiment for the content item.

Thus, in some embodiments, in Step 904, a set of user clusters can beidentified based on the NLP and sentiment analysis modelling.

In Step 906, each identified user cluster, and the items includedtherein, can be analyzed, and as a result, in Step 908, sentiment datafor each item is determined. Steps 906 and 908 can be performed in asimilar manner as discussed above in relation to Step 904.

In Step 908, a sentiment score for each content item is determined,which is based on the determined sentiment data. In some embodiments,Step 908 can be performed by engine 200 executing a TD/IDF (TermFrequency/Inverse Document Frequency) algorithm, which can quantifyterms (that correspond to particular sentiments) and determine how oftenthey occur in the set of user clusters.

In some embodiments, for example, sentiments can be related by termssuch as, but not limited to, light-hearted, dark, humorous, melancholy,optimistic, pessimistic, liberal, conservative, and the like. In Step908, it can be determined how often these sentiment terms are found inrelation to the content items in the user clusters (and selected contentitem), and for the sentiment terms with an occurrence satisfying athreshold number of times (e.g., 100 times or more), they can beidentified.

In Step 910, the identified sentiment terms satisfying the threshold canbe compiled into a listing (with the terms not satisfying the thresholdbeing filtered out), and the listing can be ranked based on the scoring(e.g., terms with higher occurrences can be ranked higher than thosewith less occurrences).

In some embodiments, Step 910 can involve displaying terms with theiropposite sentiments. This can be performed when a list of sentiments islarger than a threshold value. For example, if there are more than nsentiment terms satisfying the threshold (e.g., 150 words occurring atleast 100 times), then each sentiment can be displayed in conjunctionwith their anti-sentiment (e g, lighthearted/dark, humorous/melancholy,optimistic/pessimistic, and the like).

In Step 912, the generated list of sentiment terms can be presented tothe user, which can be interacted with to select and/or removeparticular sentiments. In some embodiments, the list can be provided asa drop-down menu, sidebar, overlay or pop-up window, or any other typeof interactive window or UI that can be displayed on UE.

In Step 914, the selected and/or unselected but not removed sentimentterms from the list can then be utilized to search for and identify newcontent recommendations to the user. In a similar manner as discussedabove, the information related to the new content recommendation and thesentiment data from Step 912 can be stored and used to train a new modeland/or re-train the recommendation model, as in Step 916.

Thus, Process 900 enables users to escape recommendations that are tiedto particular types of sentiments so as to engage in content that emitsand/or emotes other types of moods.

In FIG. 10 , Process 1000 is disclosed which provides a non-limitingexample embodiment for performing a “random” un-biasing request (item510 of FIG. 5 , as discussed above). According to some embodiments,Steps 1002-1006 of Process 1000 can be performed by explore module 204of recommendation engine 200 (and provide non-limiting exampleembodiments of Steps 306-312 of Process 300); and Steps 1008-1010 can beperformed by output module 208.

Process 1000 begins with Step 1002 where an input can be provided by theuser that requests a “random” depersonalization. In some embodiments,this can involve identifying a set of content items that are not withina user's current result set and/or are randomly selected by a randomizedalgorithm (e.g., Monte Carlo algorithms or Las Vegas algorithms, forexample, where inputs are randomized). For example, Step 1002 caninvolve a user selecting item 510 from UI 500, as discussed above.

In some embodiments, Process 1000 can proceed from Step 1002 to Step1004, where a new search is performed that randomly identifies a set ofcontent items. In some embodiments, the new search can be performed by arandomized algorithm executed by engine 200, as discussed above.

In some embodiments, Process 1000 can proceed from Step 1002 to Step1006, where rather than performing a new search, the content itemspreviously provided can be randomized. This can also be performed by arandomization algorithm, as discussed above, and in some embodiments,can include the inclusion of some existing content items from theprevious recommendation. In some embodiments, Step 1006 can involveexecuting a randomization algorithm with the inputs being from Step 302(as opposed to Step 1004 being executed with randomized inputs).

Steps 1004 and 1006 each proceed to Step 1008, where a new set ofcontent recommendations can be presented to the user. In a similarmanner as discussed above, the information related to the new contentrecommendation and the randomized search (from Steps 1004 and 1006) canbe stored and used to train a new model and/or re-train therecommendation model, as in Step 1010.

In FIG. 11 , Process 1100 is disclosed which provides a non-limitingexample embodiment for performing an un-biasing request based on“exposure”. “Exposure,” as used herein, corresponds to how exposed orinteracted with content items are on a network, which can be accordingto a specific set of users, geographic regions and/or over periods oftime, and the like. In some embodiments, such exposure, therefore, cancorrespond to popularity and/or whether the content is trending, asindicated in item 512 and item 514, respectively. Thus, interaction withitem 512 or item 514 can trigger Process 1100.

In some embodiments, exposure can also include, but is not limited to,frequency, shares, downloads, uploads, views, likes, dislikes, and/orany other type of information that indicates how popular or viewed acontent item is on a network.

According to some embodiments, Steps 1102-1108 of Process 1100 can beperformed by explore module 204 of recommendation engine 200 (andprovide non-limiting example embodiments of Steps 306-312 of Process300); and Steps 1110-1112 can be performed by output module 208.

Process 1100 begins with Step 1102 where an input can be provided by theuser that requests an “exposure” depersonalization. In Step 1104, a newsearch is performed based on exposure data and its results are providedto a user. For example, if exposure corresponds to popularity, then thetop n most popular content items can be retrieved for presentation(which can be current, or another time period). In some embodiments,popularity data can be filtered according to user preferences and/or acategory of content. In another non-limiting example, if exposurecorresponds to trending, then the top n content items that are currentlytrending can be retrieved for presentation. A trending criteria, in asimilar manner to popularity, can be tied to user preferences and/or acategory of content, and can be for a particular time period.

In Step 1106, input is received from the user that either selects and/orrejects at least a portion of the new content recommendations from Step1104. The selection and/or rejection is similar to the selection orrejection (or removal) of content items discussed above.

In Step 1108, based on the content items remaining in the providedresults after the selection and/or removal from Step 1106, a new tastevector for the user can be compiled. As discussed above, this can be an-dimensional vector that is translated from the actions of the user inview of the presented content items (e.g., selection) and/or the dataand/or metadata of those content items. In some embodiments, a currenttaste vector for a user can be adjusted based on this data, which can beperformed in a similar manner as discussed above.

In Step 1110, a new search is performed based on the new taste vector,and is presented to the user as a set of content recommendations. In asimilar manner as discussed above, the information related to the newcontent recommendation and the new/adjusted taste vector (from 1108) canbe stored and used for further training of the vectors and/orrecommendation models, as in Step 1112.

In FIG. 12 , Process 1200 is disclosed which provides a non-limitingexample embodiment for performing an un-biasing request based on a“timeline”. A timeline corresponds to a displayable set of interfaceobjects that provides a user with a visibly consumable rendering of theinformation that is used to compile the user's current taste vector(s)(e.g., the user's vector data that is used as a basis for the user'srecommendation model, as discussed above). For example, the informationcan indicate, but is not limited to, content types, sources, time, date,frequency, views, actions taken thereon, and the like, and can besequentially ordered and displayed according to how and when a userinteracted with such data. An example of a timeline is depicted in FIG.13 , as discussed below.

According to some embodiments, Steps 1202-1210 of Process 1200 can beperformed by explore module 204 of recommendation engine 200 (andprovide non-limiting example embodiments of Steps 306-312 of Process300); and Steps 1212-1214 can be performed by output module 208.

Process 1200 begins with Step 1202 where an input can be provided by theuser that requests a “timeline” depersonalization. For example,interaction with item 516 can trigger Step 1202.

In Step 1204, a user profile and/or collection of stored data related tothe user's activities (e g., both network and real-world data) can beidentified. This data can be analyzed and vectors and the dataassociated therewith that correspond to the user's currently understoodtastes/interests can be identified.

In Step 1206, the identified data can then be formulated/compiled into atimeline of activity for the user.

For example, as depicted in FIG. 13 , upon interacting with timelineitem 516 on UI 500, UI 1300 can be presented to the user, which providesa timeline of taste/interest information of the user. The non-limitingexample illustrated in FIG. 13 provides a snapshot of activity for theuser from November 2020 to April 2021, and includes the data items1302-1316, which can be interactive, deep-linking interface objects thateach correspond to networked activity of the user at a specific time.

In Step 1208, the user can interact with the timeline to provide inputcorresponding to particular items the user desires to focus on as afiltering search. In some embodiments, the interactions the user canperform can include, but are not limited to, scrolling, enlarging,zooming out/in, clicking on, navigating to additionally related content,and the like, or some combination thereof.

For example, as depicted in FIG. 13 , the user can click on item 1306,which can enable a pop-up window to display data and/or metadata relatedto that content item (e.g., category, time, source, attributes,sentiment, exposure, clusters, and the like). This can provide, forexample, information about the content item as well as informationrelated to the user's interaction with the content item that caused itto be included in the timeline.

In some embodiments, the user can select or remove at least onedisplayed item on the timeline. For example, as depicted in FIG. 13 ,the user can selected items 1306-1312 (as indicated by item 1318), andunselected items 1302, 1304, 1314 and 1316 may be discarded in anothernon-limiting example, the user can provide input to remove the itemsassociated with items 1318, whereby the timeline will be modified toonly display items 1302, 1304, 1314 and 1316.

Thus, in Step 1210, the user's current taste vector(s) can be modified(or adjusted) based on the data and/or metadata remaining (orun-discarded) in the timeline, which is a by-product of the selectionsand/or removals of items from Step 1208. In some embodiments, themodified timeline can be translated to a vector, which can then be usedas a basis for modifying the user's taste vector(s). The modification(or adjustment) of the existing taste vector(s) can be performed in asimilar manner as discussed above for other modifications/adjustments.

In Step 1212, a new search for content is generated and performed basedon the modified taste vectors, and its results are presented to theuser. In a similar manner as discussed above, the information related tothe new content recommendation and the new/adjusted taste vector(s)(from 1210) can be stored (as in Step 314) and used to train a new modeland/or re-train the recommendation model, as in Step 1214.

FIG. 14 is a block diagram illustrating a computing device showing anexample of a client or server device used in the various embodiments ofthe disclosure.

The computing device 1400 may include more or fewer components thanthose shown in FIG. 14 , depending on the deployment or usage of thedevice 1400. For example, a server computing device, such as arack-mounted server, may not include audio interfaces 1452, displays1454, keypads 1456, illuminators 1458, haptic interfaces 1462, GPSreceivers 1464, or cameras/sensors 1466. Some devices may includeadditional components not shown, such as graphics processing unit (GPU)devices, cryptographic co-processors, artificial intelligence (AI)accelerators, or other peripheral devices.

As shown in FIG. 14 , the device 1400 includes a central processing unit(CPU) 1422 in communication with a mass memory 1430 via a bus 1424. Thecomputing device 1400 also includes one or more network interfaces 1450,an audio interface 1452, a display 1454, a keypad 1456, an illuminator1458, an input/output interface 1460, a haptic interface 1462, anoptional global positioning systems (GPS) receiver 1464 and a camera(s)or other optical, thermal, or electromagnetic sensors 1466. Device 1400can include one camera/sensor 1466 or a plurality of cameras/sensors1466. The positioning of the camera(s)/sensor(s) 1466 on the device 1400can change per device 1400 model, per device 1400 capabilities, and thelike, or some combination thereof.

In some embodiments, the CPU 1422 may comprise a general-purpose CPU.The CPU 1422 may comprise a single-core or multiple-core CPU. The CPU1422 may comprise a system-on-a-chip (SoC) or a similar embedded system.In some embodiments, a GPU may be used in place of, or in combinationwith, a CPU 1422. Mass memory 1430 may comprise a dynamic random-accessmemory (DRAM) device, a static random-access memory device (SRAM), or aFlash (e.g., NAND Flash) memory device. In some embodiments, mass memory1430 may comprise a combination of such memory types. In one embodiment,the bus 1424 may comprise a Peripheral Component Interconnect Express(PCIe) bus. In some embodiments, the bus 1424 may comprise multiplebusses instead of a single bus.

Mass memory 1430 illustrates another example of computer storage mediafor the storage of information such as computer-readable instructions,data structures, program modules, or other data. Mass memory 1430 storesa basic input/output system (“BIOS”) 1440 for controlling the low-leveloperation of the computing device 1400. The mass memory also stores anoperating system 1441 for controlling the operation of the computingdevice 1400.

Applications 1442 may include computer-executable instructions which,when executed by the computing device 1400, perform any of the methods(or portions of the methods) described previously in the description ofthe preceding Figures. In some embodiments, the software or programsimplementing the method embodiments can be read from a hard disk drive(not illustrated) and temporarily stored in RAM 1432 by CPU 1422. CPU1422 may then read the software or data from RAM 1432, process them, andstore them to RAM 1432 again.

The computing device 1400 may optionally communicate with a base station(not shown) or directly with another computing device. Network interface1450 is sometimes known as a transceiver, transceiving device, ornetwork interface card (NIC).

The audio interface 1452 produces and receives audio signals such as thesound of a human voice. For example, the audio interface 1452 may becoupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgment forsome action. Display 1454 may be a liquid crystal display (LCD), gasplasma, light-emitting diode (LED), or any other type of display usedwith a computing device. Display 1454 may also include a touch-sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 1456 may comprise any input device arranged to receive input froma user. Illuminator 1458 may provide a status indication or providelight.

The computing device 1400 also comprises an input/output interface 1460for communicating with external devices, using communicationtechnologies, such as USB, infrared, Bluetooth™, or the like. The hapticinterface 1462 provides tactile feedback to a user of the client device.

The optional GPS transceiver 1464 can determine the physical coordinatesof the computing device 1400 on the surface of the Earth, whichtypically outputs a location as latitude and longitude values. GPStransceiver 1464 can also employ other geo-positioning mechanisms,including, but not limited to, triangulation, assisted GPS (AGPS),E-OTD, CI, SAI, ETA, BSS, or the like, to further determine the physicallocation of the computing device 1400 on the surface of the Earth. Inone embodiment, however, the computing device 1400 may communicatethrough other components, provide other information that may be employedto determine a physical location of the device, including, for example,a MAC address, IP address, or the like.

The present disclosure has been described with reference to theaccompanying drawings, which form a part hereof, and which show, by wayof non-limiting illustration, certain example embodiments. Subjectmatter may, however, be embodied in a variety of different forms and,therefore, covered or claimed subject matter is intended to be construedas not being limited to any example embodiments set forth herein;example embodiments are provided merely to be illustrative. Likewise, areasonably broad scope for claimed or covered subject matter isintended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in some embodiments” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure has been described with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

For the purposes of this disclosure, a non-transitory computer readablemedium (or computer-readable storage medium/media) stores computer data,which data can include computer program code (or computer-executableinstructions) that is executable by a computer, in machine readableform. By way of example, and not limitation, a computer readable mediummay comprise computer readable storage media, for tangible or fixedstorage of data, or communication media for transient interpretation ofcode-containing signals. Computer readable storage media, as usedherein, refers to physical or tangible storage (as opposed to signals)and includes without limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, optical storage,cloud storage, magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, groups, or other entities,it should be understood that such information shall be used inaccordance with all applicable laws concerning the protection ofpersonal information. Additionally, the collection, storage, and use ofsuch information can be subject to the consent of the individual to suchactivity, for example, through well known “opt-in” or “opt-out”processes as can be appropriate for the situation and type ofinformation. Storage and use of personal information can be in anappropriately secure manner reflective of the type of information, forexample, through various access control, encryption, and anonymizationtechniques (for especially sensitive information).

In the preceding specification, various example embodiments have beendescribed with reference to the accompanying drawings. However, it willbe evident that various modifications and changes may be made thereto,and additional embodiments may be implemented without departing from thebroader scope of the disclosed embodiments as set forth in the claimsthat follow. The specification and drawings are accordingly to beregarded in an illustrative rather than restrictive sense.

What is claimed is:
 1. A method comprising: receiving, by a device, arequest for content from a user; providing, by the device, a set ofpersonalized content items based on a recommendation model associatedwith the user in response to the request for content; receiving, by thedevice, an un-biasing request from the user, the un-biasing requestcomprising information indicating a type of request to un-bias resultsoutput from the recommendation model associated with the user;analyzing, by the device, the set of personalized content items based atleast on the un-biasing request; identifying, by the device, based onthe analyzed set of personalized content items, a new set of contentitems, at least a portion of the new set of content items comprisingcharacteristics different from characteristics of the set ofpersonalized content items; and providing, by the device over a network,the new set of content items to the user for display on a device of theuser in response to the un-biasing request.
 2. The method of claim 1,further comprising: identifying that the type of request corresponds toa given item; identifying at least one of the set of personalizedcontent items as the given item; determining a similarity group ofcontent items based on the given item, each item in the similarity groupsatisfying a similarity threshold to the given item; determining anopposite group of content items based on the given item, each item inthe opposite group failing to satisfy the similarity threshold to thegiven item; and analyzing each item in each group, and determiningsimilarities and differences between the items in each group, whereinidentification of the new set of content items is further based oninformation related to the determined similarities and differences. 3.The method of claim 2, further comprising: enabling selection andremoval of at least a portion of the items in each group, wherein theanalysis of items in each group is based on remaining items in eachgroup after the enabled selection and removal.
 4. The method of claim 1,further comprising: identifying that the type of request corresponds toan attribute; analyzing the set of personalized content items anddetermining a set of attributes, the attributes being at least one of aboolean type, categorical type and numerical type; receiving inputidentifying at least one of the set of attributes; determining a type ofthe identified attribute, wherein when the identified attribute is theboolean type, computing an escape from the personalized set ofrecommendations based on a boolean computation, when the identifiedattribute is the categorical type, computing an escape from thepersonalized set of recommendations based on a categorical computation,and when the identified attribute is the numerical type, computing anescape from the personalized set of recommendations based on a numericalcomputation; and performing the identification of the new set of contentitems based on the determined type of computation.
 5. The method ofclaim 1, further comprising: identifying that the type of requestcorresponds to a user cluster, the user cluster being associated withthe user and corresponding to a grouping of data related to a context,the user cluster having a corresponding vector; identifying a set ofother user clusters, each other user cluster corresponding to a contextthat is different than the context of the user's user cluster; receivinginput identifying at least one item from at least one other usercluster; identifying a vector associated with the identified item; andcomputing a new vector for the user based on the user cluster vector andthe identified item vector, wherein the identification of the new set ofcontent items is based on the new vector.
 6. The method of claim 1,further comprising: identifying that the type of request corresponds toa sentiment, the sentiment corresponding to a selected item from the setof personalized content items; identifying a set of user clusters basedon the sentiment; analyzing each item in each user cluster, anddetermining at least one sentiment for each item; determining asentiment score for each item based on the at least one sentimentdetermined for the item; generating a listing of sentiment terms basedon the determined sentiment scoring; and receiving interaction with atleast one sentiment term in the listing, wherein the identification ofthe new set of content items is based on the received interaction withat least one sentiment in the listing.
 7. The method of claim 1, furthercomprising: identifying that the type of request is a request to performa random search; and executing a search for content using a randomizedalgorithm to identify a new set of content items, wherein theidentification of the new set of content items is based on the search.8. The method of claim 1, further comprising: identifying that the typeof request corresponds to exposure of a selected content item, whereinexposure relates to at least one of popularity or trends of a period oftime; executing a search for content based on information related to theexposure; receiving input related to selections or rejections of atleast a portion of results of the search; and creating a new user vectorbased on the input-based results, wherein the identification of the newset of content items is based on the new user vector.
 9. The method ofclaim 1, further comprising: identifying that the type of requestcorresponds to a timeline, the timeline comprising a displayable andinteractive interface object that displays user vector data for whichthe recommendation model of the user is based; causing display of thetimeline on the device of the user; receiving information related tointeractions with the timeline by the user; and modifying the uservector data based on the received information, wherein theidentification of the new set of content items is based on the modifieduser vector data.
 10. The method of claim 1, further comprising:modifying a user interface (UI) that displays the set of personalizedcontent items to display an interface object that enables the un-biasingrequest, wherein the un-biasing request is in response to userinteraction with the interface object; and causing a display of a set ofinterface objects that enables identification of the type of request,wherein the analysis of the set of personalized content items is furtherbased on the type of request.
 11. The method of claim 1, furthercomprising: storing, in a database associated with the device,information associated with the new set of content items and theun-biasing request; and further training the recommendation model basedon the stored information.
 12. A device comprising: a processorconfigured to: receive a request for content from a user; provide a setof personalized content items based on a recommendation model associatedwith the user in response to the request for content; receive anun-biasing request from the user, the un-biasing request comprisinginformation indicating a type of request to un-bias results output fromthe recommendation model associated with the user; analyze the set ofpersonalized content items based at least on the un-biasing request;identify, based on the analyzed set of personalized content items, a newset of content items, at least a portion of the new set of content itemscomprising characteristics different from characteristics of the set ofpersonalized content items; and provide, over a network, the new set ofcontent items to the user for display on a device of the user inresponse to the un-biasing request.
 13. The device of claim 12, theprocessor further configured to: identify that the type of requestcorresponds to a given item; identify at least one of the set ofpersonalized content items as the given item; determine a similaritygroup of content items based on the given item, each item in thesimilarity group satisfying a similarity threshold to the given item;determine an opposite group of content items based on the given item,each item in the opposite group failing to satisfy the similaritythreshold to the given item; and analyze each item in each group, anddetermine similarities and differences between the items in each group,wherein identification of the new set of content items is further basedon information related to the determined similarities and differences.14. The device of claim 12, the processor further configured to:identify that the type of request corresponds to an attribute; analyzethe set of personalized content items and determine a set of attributes,the attributes being at least one of a boolean type, categorical typeand numerical type; receive input identifying at least one of the set ofattributes; determine a type of the identified attribute, wherein whenthe identified attribute is the boolean type, compute an escape from thepersonalized set of recommendations based on a boolean computation, whenthe identified attribute is the categorical type, compute an escape fromthe personalized set of recommendations based on a categoricalcomputation, and when the identified attribute is the numerical type,compute an escape from the personalized set of recommendations based ona numerical computation; and perform the identification of the new setof content items based on the determined type of computation.
 15. Thedevice of claim 12, the processor further configured to: identify thatthe type of request corresponds to a user cluster, the user clusterbeing associated with the user and corresponding to a grouping of datarelated to a context, the user cluster having a corresponding vector;identify a set of other user clusters, each other user clustercorresponding to a context that is different than the context of theuser's user cluster; receive input identifying at least one item from atleast one other user cluster; identify a vector associated with theidentified item; and compute a new vector for the user based on the usercluster vector and the identified item vector, wherein theidentification of the new set of content items is based on the newvector.
 16. The device of claim 12, the processor further configured to:identify that the type of request corresponds to a sentiment, thesentiment corresponding to a selected item from the set of personalizedcontent items; identify a set of user clusters based on the sentiment;analyze each item in each cluster, and determine at least one sentimentdata for each item; determine a sentiment score for each item based onthe at least one sentiment determined for the item; generate a listingof sentiment terms based on the determined sentiment scoring; andreceive interaction with at least one sentiment term in the listing,wherein the identification of the new set of content items is based onthe received interaction with at least one sentiment in the listing. 17.The device of claim 12, the processor further configured to: identifythat the type of request is a request to perform a random search; andexecute a search for content using a randomized algorithm to identify anew set of content items, wherein the identification of the new set ofcontent items is based on the search.
 18. The device of claim 12, theprocessor further configured to: identify that the type of requestcorresponds to exposure of a selected content item, wherein exposurerelates to at least one of popularity or trends of a period of time;execute a search for content based on information related to theexposure; receive input related to selections or rejections of at leasta portion of results of the search; and create a new user vector basedon the input-based results, wherein the identification of the new set ofcontent items is based on the new user vector.
 19. The device of claim12, the processor further configured to: identify that the type ofrequest corresponds to a timeline, the timeline comprising a displayableand interactive object that displays user vector data for which therecommendation model of the user is based; cause display of the timelineon the device of the users' receive information related to interactionswith the timeline by the user; and modify the user vector data based onthe received information, wherein the identification of the new set ofcontent items is based on the modified user vector data.
 20. Anon-transitory computer-readable medium tangibly encoded withinstructions, that when executed by a processor of a device, perform amethod comprising: receiving, by the device, a request for content froma user; providing, by the device, a set of personalized content itemsbased on a recommendation model associated with the user in response tothe request for content; receiving, by the device, an un-biasing requestfrom the user, the un-biasing request comprising information indicatinga type of request to un-bias results output from the recommendationmodel associated with the user; analyzing, by the device, the set ofpersonalized content items based at least on the un-biasing request;identifying, by the device, based on the analyzed set of personalizedcontent items, a new set of content items, at least a portion of the newset of content items comprising characteristics different fromcharacteristics of the set of personalized content items; and providing,by the device over a network, the new set of content items to the userfor display on a device of the user in response to the un-biasingrequest.